Database sharding strategies in HLD - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is database sharding?

Database sharding is a technique to split a large database into smaller, faster, and more manageable parts called shards. Each shard holds a portion of the data, helping improve performance and scalability.

beginner

Explain horizontal sharding.

Horizontal sharding divides data by rows. Each shard contains a subset of rows from the same table, often based on a key like user ID. This spreads the load across multiple servers.

intermediate

What is vertical sharding and when is it used?

Vertical sharding splits data by columns or tables. Different shards hold different parts of the schema. It is useful when some tables or columns are accessed more frequently or require different resources.

intermediate

Describe directory-based sharding.

Directory-based sharding uses a lookup service to map each data item to its shard. This allows flexible shard assignment but adds a dependency on the directory for every query.

advanced

What are common challenges of database sharding?

Challenges include data rebalancing when shards grow unevenly, handling cross-shard queries, maintaining consistency, and increased complexity in application logic.

Which sharding strategy splits data by rows across multiple shards?

A Directory-based sharding

B Vertical sharding

C Horizontal sharding

D Functional sharding

Horizontal sharding divides data by rows, distributing subsets of rows across shards.

What is a key disadvantage of directory-based sharding?

A It duplicates data across shards

B It cannot handle large datasets

C It only works with vertical sharding

D It requires a lookup service for every query

Directory-based sharding depends on a lookup service to find the shard, adding overhead and a potential single point of failure.

Vertical sharding splits data based on:

A Columns or tables

B User IDs

C Rows

D Random distribution

Vertical sharding divides data by columns or tables, separating parts of the schema into different shards.

Which problem is NOT typically caused by sharding?

A Cross-shard query complexity

B Automatic data backup

C Increased application complexity

D Data rebalancing

Automatic data backup is not a direct problem caused by sharding; it is usually handled separately.

What is the main goal of database sharding?

A To improve performance and scalability by splitting data

B To reduce database size by deleting data

C To encrypt data for security

D To merge multiple databases into one

Sharding splits data into smaller parts to improve performance and allow the system to scale.

Describe the main types of database sharding strategies and when to use each.

Think about how data is divided: by rows, columns, or using a directory.

What challenges arise from implementing database sharding and how can they affect system design?

Consider operational and development difficulties after splitting data.

Practice

1. Which of the following best describes database sharding?

easy

A. Backing up the entire database to multiple locations for safety.

B. Splitting a large database into smaller, faster, more manageable parts called shards.

C. Encrypting the database to protect sensitive data.

D. Merging multiple databases into one large database.

Solution

1. Step 1: Understand the concept of sharding

   Sharding means dividing a database into smaller parts to improve speed and manageability.

2. Step 2: Compare options with the definition

   Only Splitting a large database into smaller, faster, more manageable parts called shards. describes splitting into smaller parts called shards, which matches the definition.

3. Final Answer:

   Splitting a large database into smaller, faster, more manageable parts called shards. -> Option B

4. Quick Check:

   Sharding = splitting database [OK]

Hint: Sharding means splitting data into parts [OK]

Common Mistakes:

• Confusing sharding with backup or encryption
• Thinking sharding merges databases
• Assuming sharding is only about security

2. Which of the following is a valid shard key choice for hash-based sharding?

easy

A. User ID hashed to assign shard

B. Timestamp of record creation

C. Database server IP address

D. Number of active connections

Solution

1. Step 1: Identify shard key for hash sharding

   Hash sharding uses a hash function on a key like User ID to distribute data evenly.

2. Step 2: Evaluate options

   User ID is a stable, unique key suitable for hashing. Timestamp or server IP are not good shard keys for hashing.

3. Final Answer:

   User ID hashed to assign shard -> Option A

4. Quick Check:

   Hash shard key = User ID [OK]

Hint: Hash shard keys are stable unique IDs [OK]

Common Mistakes:

• Using timestamps which cause uneven shard distribution
• Choosing server IP which is unrelated to data
• Picking dynamic values like active connections

3. Given a range-based sharding on user age groups: shard 1 for ages 0-20, shard 2 for 21-40, shard 3 for 41-60. Which shard will a user aged 35 be stored in?

medium

A. Shard 3

B. Shard 1

C. Shard 2

D. None of the above

Solution

1. Step 1: Understand the age ranges for each shard

   Shard 1: 0-20, Shard 2: 21-40, Shard 3: 41-60.

2. Step 2: Determine which range 35 falls into

   Age 35 is between 21 and 40, so it belongs to Shard 2.

3. Final Answer:

   Shard 2 -> Option C

4. Quick Check:

   35 in 21-40 = Shard 2 [OK]

Hint: Check which range the value fits [OK]

Common Mistakes:

• Choosing shard 1 by mistake for age 35
• Confusing shard 3 range
• Selecting 'None of the above' incorrectly

4. A system uses hash-based sharding on user email addresses. Recently, many users with emails starting with 'a' are causing one shard to overload. What is the likely problem?

medium

A. The hash function is not distributing keys evenly.

B. Emails starting with 'a' are stored in a separate shard by design.

C. Range sharding was used instead of hash sharding.

D. The shard key is not unique.

Solution

1. Step 1: Analyze the shard overload cause

   Hash-based sharding should distribute keys evenly. Overload on one shard means uneven distribution.

2. Step 2: Evaluate options

   The hash function is not distributing keys evenly. explains uneven hash distribution. Emails starting with 'a' are stored in a separate shard by design. is incorrect because hash sharding doesn't group by prefix. Range sharding was used instead of hash sharding. is wrong as range sharding is not used. The shard key is not unique. is unrelated to overload.

3. Final Answer:

   The hash function is not distributing keys evenly. -> Option A

4. Quick Check:

   Uneven hash distribution = overload [OK]

Hint: Uneven load means bad hash distribution [OK]

Common Mistakes:

• Assuming range sharding was used
• Thinking shard key uniqueness causes overload
• Believing emails with same prefix go to same shard by hash

5. You need to shard a global user database to minimize latency and balance load. Which strategy is best to combine geographic location and user ID for sharding?

hard

A. Use range sharding on user ID across all regions.

B. Use only hash sharding on user ID ignoring location.

C. Use directory sharding mapping user ID to random shards.

D. Use geographic sharding first, then hash shard within each region by user ID.

Solution

1. Step 1: Understand the goal

   Minimize latency by serving users from nearby data centers and balance load within each region.

2. Step 2: Evaluate sharding strategies

   Geographic sharding groups users by location to reduce latency. Hash sharding inside each region balances load evenly.

3. Step 3: Compare options

   Use geographic sharding first, then hash shard within each region by user ID. combines geographic and hash sharding effectively. Use only hash sharding on user ID ignoring location. ignores location, increasing latency. Use range sharding on user ID across all regions. uses range sharding which may cause hotspots. Use directory sharding mapping user ID to random shards. uses random mapping, ignoring location and load balance.

4. Final Answer:

   Use geographic sharding first, then hash shard within each region by user ID. -> Option D

5. Quick Check:

   Geo + hash sharding = low latency + balanced load [OK]

Hint: Combine geo then hash for best latency and balance [OK]

Common Mistakes:

• Ignoring geographic location causing high latency
• Using only hash sharding causing uneven latency
• Using range sharding causing load hotspots